विधियों की तुलना करें

	मोंटे कार्लो सिमुलेशन	स्टोकेस्टिक संवेदनशीलता विश्लेषण
क्षेत्र	निर्णयन	अनुकरण
परिवार	MCDM	Process / pipeline
उद्भव वर्ष	1949	1990s–2000s
प्रवर्तक	Metropolis, N., Ulam, S.	Saltelli, A. et al.; Claxton, K. et al. (health economics stream)
प्रकार	Robustness wrapper — Monte Carlo uncertainty propagation	Probabilistic uncertainty quantification technique
मौलिक स्रोत	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 9780470059975
उपनाम	—	PSA, Probabilistic Sensitivity Analysis, Stochastic SA, Monte Carlo Sensitivity Analysis
संबंधित	0	5
सारांश	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	Stochastic Sensitivity Analysis (PSA) extends classical one-at-a-time sensitivity testing by representing uncertain model inputs as probability distributions and propagating them through the model via Monte Carlo sampling. The result is a full distribution of possible outputs, together with rankings of which inputs drive output variance the most — enabling robust, evidence-grounded conclusions under uncertainty.